A recent review of solution approaches for green vehicle routing problem and its variants

Operations Research Perspectives, Journal Year: 2024, Volume and Issue: 12, P. 100303 - 100303

Published: April 28, 2024

The green vehicle routing problem (GVRP) has been a prominent topic in the literature on logistics and transportation, leading to extensive research previous review studies covering various aspects. Operations seen development of exact approximation approaches for different extensions GVRP. This paper presents an up-to-date thorough GVRP spanning from 2016 2023, encompassing 458 papers. significant contribution lies updated solution algorithms applied both single-objective multi-objective Notably, 92.58% papers introduced mathematical model GVRP, with many researchers adopting mixed integer linear programming as preferred modeling approach. findings indicate that metaheuristics hybrid are most employed addressing Among approaches, combination metaheuristics-metaheuristics is particularly favored by researchers. Furthermore, large neighborhood search (LNS) its variants (especially adaptive search) emerges widely adopted algorithm These proposed within metaheuristic where A-/LNS often combined other algorithms. Conversely, predominant NSGA-II being frequently algorithm. Researchers utilize GAMS CPLEX optimization software solvers. MATLAB commonly language implementing

Language: Английский

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CTOA: Toward a Chaotic-Based Tumbleweed Optimization Algorithm

Mathematics, Journal Year: 2023, Volume and Issue: 11(10), P. 2339 - 2339

Published: May 17, 2023

Metaheuristic algorithms are an important area of research in artificial intelligence. The tumbleweed optimization algorithm (TOA) is the newest metaheuristic that mimics growth and reproduction tumbleweeds. In practice, chaotic maps have proven to be improved method algorithms, allowing jump out local optimum, maintain population diversity, improve global search ability. This paper presents a chaotic-based (CTOA) incorporates into process TOA. By using 12 common maps, proposed CTOA aims diversity exploration prevent from falling optima. performance tested 28 benchmark functions CEC2013, results show circle map most effective improving accuracy convergence speed CTOA, especially 50D.

Language: Английский

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A two-echelon sustainable multi-route location routing problem for biomass supply chain network design considering disruption

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 187, P. 108744 - 108744

Published: May 23, 2024

Language: Английский

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Edge-Driven Multiple Trajectory Attention Model for Vehicle Routing Problems

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2679 - 2679

Published: March 2, 2025

The vehicle routing problem (VRP), as one of the classic combinatorial optimization problems, has garnered widespread attention in recent years. Existing deep reinforcement learning (DRL)-based methods predominantly focus on node information, neglecting edge information inherent graph structure. Moreover, solution trajectories produced by these tend to exhibit limited diversity, hindering a thorough exploration space. In this work, we propose novel Edge-Driven Multiple Trajectory Attention Model (E-MTAM) solve VRPs with various scales. Our model is built upon encoder–decoder architecture, incorporating an edge-driven multi-head (EDMHA) block within encoder better utilize information. During decoding process, enhance embeddings visitation integrating dynamic updates into static embeddings. Additionally, employ multi-decoder architecture and introduce regularization term encourage generation diverse trajectories, thus promoting diversity. We conduct comprehensive experiments three types VRPs: (1) traveling salesman (TSP), (2) capacitated (CVRP), (3) orienteering (OP). experimental results demonstrate that our outperforms existing DRL-based most traditional heuristic approaches, while also exhibiting strong generalization across problems different

Language: Английский

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2024, Volume and Issue: 79(1), P. 19 - 46

Published: Jan. 1, 2024

Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving highprecision remains significant challenge.In response to this challenge, Spectral Convolutional Neural Network model based on Adaptive Fick's Law Algorithm (AFLA-SCNN) is proposed.The (AFLA) constitutes novel metaheuristic algorithm introduced herein, encompassing three new strategies: weight factor, Gaussian mutation, and probability update policy.With adaptive can adjust weights according change in number iterations improve performance algorithm.Gaussian mutation helps avoid falling into local optimal solutions improves searchability algorithm.The strategy exploitability adaptability algorithm.Within AFLA-SCNN model, AFLA employed optimize two hyperparameters SCNN namely, "numEpochs" "miniBatchSize", attain their values.AFLA's initially validated across 28 functions 10D, 30D, 50D for CEC2013 29 CEC2017.Experimental results indicate AFLA's marked superiority over nine other prominent optimization algorithms.Subsequently, was compared with (FLA-SCNN), Harris Hawks Optimization (HHO-SCNN), Differential Evolution (DE-SCNN), (SCNN) Support Vector Machines (SVM) using Indian Pines dataset Pavia University dataset.The experimental show that outperforms models terms Accuracy, Precision, Recall, F1-score University.Among them, Accuracy reached 99.875%, 98.022%.In conclusion, our proposed deemed significantly enhance precision hyperspectral classification.

Language: Английский

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A Hybrid Particle Swarm Optimization with Tabu Search for Optimizing Aid Distribution Route

Artificial Intelligence Studies, Journal Year: 2024, Volume and Issue: 7(1), P. 10 - 27

Published: July 10, 2024

This paper explores the use of metaheuristic algorithms for Multi-Depot Vehicle Routing Problem, a complex form Problem crucial in logistics.The study contributes to operational research, offering strategies effective logistics management and underscores significance tackling intricate optimization problems.The focuses on optimizing vehicle routes from multiple depots, using k-clustering technique initial grouping.It examines like Particle Swarm Optimization, Artificial Bee Colony, Ant Colony hybrid Optimization with Tabu Search.These are vital efficient route planning varied environments, practical implications demonstrated real-world scenarios.The findings revealed limitations PSO algorithm showed improvement Search.While, resulting hybrid, Search, remarkable improvements stands out its efficiency reliability it underscored potential solving Nondeterministic Polynomial Time -hard combinatorial problems.Yardım Dağıtım Rotası Optimizasyonu için Arama ile Hibrit Parçacık Sürü ÖZ Bu makale, lojistikte hayati öneme sahip Araç Rotalama Probleminin karmaşık bir formu olan Çok Depolu Problemi metasezgisel algoritmaların kullanımını araştırmaktadır

Language: Английский

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A Hybrid Parallel Willow Catkin Optimization Algorithm Applied for Engineering Optimization Problems

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 102396 - 102415

Published: Jan. 1, 2024

The Willow Catkin Optimization Algorithm (WCO) is a newly proposed meta-heuristic algorithm in recent years that has simple structure and excellent optimization searching ability, but the WCO could benefit from improvements both convergence speed solution diversity. In this paper, parallel technology introduced into algorithm, by proposing two new communication strategies, Random Mean (RM) method Optimal Flight (OF) method, goal to utilize all information obtained each subpopulation strategy enhance algorithm's performance. Additionally, been hybridized with Differential Evolution (DE), mutation mechanism improve diversity of solutions. resulting called Hybrid Parallel (HPWCO). HPWCO tested on CEC2017 benchmark function set applied five real-world engineering problems constraints, experimental results were compared three types algorithms: classical algorithm. indicate performs excellently.

Language: Английский

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Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop

Symmetry, Journal Year: 2023, Volume and Issue: 15(8), P. 1505 - 1505

Published: July 28, 2023

In this study, we present and discuss a variant of the classic vehicle routing problem (VRP), green automated guided (AGV) problem, which involves simultaneous pickup delivery with time windows (GVRPSPDTW) in an intelligent workshop. The research object is AGV energy consumption. First, conduct comprehensive analysis mechanical forces during transportation evaluate overall operational efficiency Then, construct path planning model to minimize consumption standby period. Hence, problems considered study are modeled based on asymmetry, making solving more complex. We also design hybrid differential evolution algorithm large neighborhood search (DE-LNS) increase local ability algorithm. To enhance optimal quality solutions, adaptive scaling factor use squirrel migration operator optimize population. Last, extensive computational experiments, generated from VRPSPDTW instances set real case workshop, designed demonstrate effectiveness proposed experimental results show that DE-LNS yields competitive results, compared advanced heuristic algorithms. applicability verified. Additionally, demonstrates significant energy-saving potential workshop logistics. It can by 15.3% traditional model. Consequently, carries substantial implications for minimizing total costs exhibits promising prospects real-world application workshops.

Language: Английский

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A Multigoal Path-Planning Approach for Explosive Ordnance Disposal Robots Based on Bidirectional Dynamic Weighted-A* and Learn Memory-Swap Sequence PSO Algorithm

Symmetry, Journal Year: 2023, Volume and Issue: 15(5), P. 1052 - 1052

Published: May 9, 2023

In order to protect people’s lives and property, increasing numbers of explosive disposal robots have been developed. It is necessary for an ordinance (EOD) robot quickly detect all explosives, especially when the location explosives unknown. To achieve this goal, we propose a bidirectional dynamic weighted-A star (BD-A*) algorithm learn memory-swap sequence particle swarm optimization (LM-SSPSO) algorithm. Firstly, in BD-A* algorithm, our aim obtain shortest distance path between any two goal positions, considering computation time optimization. We optimize by introducing search OpenList cost weight strategy. Secondly, search-adjacent nodes are extended shorter path. Thirdly, using LM-SSPSO plan that traverses positions. The problem similar symmetric traveling salesman (TSP). introduce swap strategy into traditional PSO whole process imitating human learning memory behaviors. Fourthly, verify performance proposed begin comparing improved A* with over different resolutions, coefficients, nodes. hybrid also compared other intelligent algorithms. Finally, environment maps discussed further simulation results demonstrate has superior finding less computational time. LM-SSPSO, convergence rate significantly improves, more likely optimal

Language: Английский

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Path Planning Algorithm Based on Obstacle Clustering Analysis and Graph Search

Symmetry, Journal Year: 2023, Volume and Issue: 15(8), P. 1498 - 1498

Published: July 28, 2023

Path planning is receiving considerable interest in mobile robot research; however, a large number of redundant nodes are typically encountered the path search process for large-scale maps, resulting decreased algorithmic efficiency. To address this problem, paper proposes graph algorithm that based on map preprocessing creating weighted map, thus obtaining structured framework. In addition, reductions DBSCAN were analyzed. Subsequently, optimal combination minPts and Eps required to achieve an efficient accurate clustering obstacle communities was determined. The effective edge points then found by performing collision detection between special grid nodes. A straight-line connection or A* strategy used establish weighted, undirected contained start end points, thereby achieving This approach reduces impact scale time cost improves efficiency planning. results simulation experiments indicate be calculated decreases significantly when using proposed algorithm, improving offers excellent performance maps with few obstacles.

Language: Английский

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